Deciphering seizure semiology in corpus callosum injuries: A comprehensive systematic review with machine learning insights.

INTRODUCTION: Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delineating this entity. OBJECTIVE: This systematic review aims to analyze the effects of corpus callosum injuries on seizure semiology, providing insights into the neuroscientific and clinical implications of such injuries. METHODS: Adhering to the PRISMA guidelines, a comprehensive search across multiple databases, including PubMed/Medline, NIH, Embase, Cochrane Library, and Cross-ref, was conducted until September 25, 2023. Studies on seizures associated with corpus callosum injuries, excluding other cortical or sub-cortical involvements, were included. Machine learning (Random Forest) and deep learning (1D-CNN) algorithms were employed for data classification. RESULTS: Initially, 1250 articles were identified from the mentioned databases, and additional 350 were found through other relevant sources. Out of all these articles, 41 studies met the inclusion criteria, collectively encompassing 56 patients The most frequent clinical manifestations included generalized tonic-clonic seizures, complex partial seizures, and focal seizures. The most common callosal injuries were related to reversible splenial lesion syndrome and cytotoxic lesions. Machine learning and deep learning analyses revealed significant correlations between seizure types, semiological parameters, and callosal injury locations. Complete recovery was reported in the majority of patients post-treatment. CONCLUSION: Corpus callosum injuries have diverse impacts on seizure semiology. This review highlights the importance of understanding the role of the corpus callosum in seizure propagation and manifestation. The findings emphasize the need for targeted diagnostic and therapeutic strategies in managing seizures associated with callosal injuries. Future research should focus on expanding the data pool and exploring the underlying mechanisms in greater detail.

Constant community identification in million-scale networks.

The inherently stochastic nature of community detection in real-world complex networks poses an important challenge in assessing the accuracy of the results. In order to eliminate the algorithmic and implementation artifacts, it is necessary to identify the groups of vertices that are always clustered together, independent of the community detection algorithm used. Such groups of vertices are called constant communities. Current approaches for finding constant communities are very expensive and do not scale to large networks. In this paper, we use binary edge classification to find constant communities. The key idea is to classify edges based on whether they form a constant community or not. We present two methods for edge classification. The first is a GCN-based semi-supervised approach that we term Line-GCN. The second is an unsupervised approach based on image thresholding methods. Neither of these methods requires explicit detection of communities and can thus scale to very large networks of the order of millions of vertices. Both of our semi-supervised and unsupervised results on real-world graphs demonstrate that the constant communities obtained by our method have higher F1-scores and comparable or higher NMI scores than other state-of-the-art baseline methods for constant community detection. While the training step of Line-GCN can be expensive, the unsupervised algorithm is 10 times faster than the baseline methods. For larger networks, the baseline methods cannot complete, whereas all of our algorithms can find constant communities in a reasonable amount of time. Finally, we also demonstrate that our methods are robust under noisy conditions. We use three different, well-studied noise models to add noise to the networks and show that our results are mostly stable.